Tidy Tuesday 2

Load Libraries

say("Eat Mor Chikin", by = "cow")
## 
##  ----- 
## Eat Mor Chikin 
##  ------ 
##     \   ^__^ 
##      \  (oo)\ ________ 
##         (__)\         )\ /\ 
##              ||------w|
##              ||      ||

Load Data

tuesdata <- tt_load("2021-02-16") # Load census data of persons
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Census <- tuesdata$census

View(Census)

Introduction

The goal of this second Tidy Tuesday is to create a pie chart distribution of the average number of persons per race from the years 1790 - 1830 and from 1831 - 1870.

I will start by learning how to create a pi chart and include my examples to keep record of my progress. Skip to “Create pi chart using the Census data” for my figures.

If I am unable to do this, I will create a single pie chart of all years.

Learing how to create a pi chart

Functions:

“x” is a vector containing the numeric values used in the pie chart

“labels” is used to give description to the slices.

“main” indicates the title of the chart

“col” indicates the color palette

“clockwise” is a logical value indicating if the slices are drawn clockwise or counterclockwise

“int.angle” determines the starting angle of the slices, from 0 to 360.

“$” dollar sign is a way to reference a column.

Practicing using pie chart function

color<- brewer.pal(length(count), "Set1") #define color
## Warning in brewer.pal(length(count), "Set1"): minimal value for n is 3, returning requested palette with 3 different levels

Example

count<- c(7, 25, 16, 12, 10,30)

pie(count, #creates pie chart as 1 - 6 counterclockwise 
    clockwise = TRUE, #makes clockwise
    labels = count, #shows actual values of counts
    col = color)

Creating a 3D pi Chart

library(plotrix) 
slices <- c(10, 12, 4, 16, 8) #assigning random numbers 
lbls <- c("US", "UK", "Australia", "Germany", "France") #Naming slices
pie3D(slices,labels=lbls,explode=0.1, # makes pi charts 3D
   main="Pie Chart of Countries ") #giving title

Creating pie chart with example data

# Pie Chart from data frame with Appended Sample Sizes
mytable <- table(iris$Species)
lbls <- paste(names(mytable), "\n", mytable, sep="")

pie(mytable, labels = lbls,
   main="Pie Chart of Species\n (with sample sizes)")

view (iris)

view(mytable)

Create pi chart using the Census data

Pivot data

view(Census)

R_pivot <- Census %>% 
  pivot_longer(cols = total:black_slaves, # the cols you want to pivot. This says select the all the types of persons positions/ status's. 
                 names_to = "Person_Status", # put the names of all person's status under one column
               values_to = "Values") %>% # put the number of persons in each catergory under values column
  filter(region == "USA Total") #look at national level data only 

view(R_pivot)

Manipulate data for two different time periods

data<- R_pivot %>%
  filter(year == 1790, 
         Person_Status != "total") #filter for the year 1790 and exclude total number


data2<- R_pivot %>%
  filter(year == 1870,
         Person_Status != "total") #filter for the year 1870 and exclude total number

Create Pi Charts

# Pie chart with plotly for all person's statuses in 1790

a <- plot_ly(data = data, labels = ~Person_Status, values = ~Values, # load in data, labels to status, values to number of persons under each category
             type = 'pie', sort= FALSE, #create pie chart
            marker= list(colors=colors, line = list(color="black", width=1))) %＞% #fills in pie slices
            layout(title="Person's Status in the United States, 1790") #add title

a 
# Pie chart with plotly for all person's statuses in 1870

b <- plot_ly(data = data2, labels = ~Person_Status, values = ~Values, # load in data, labels to status, values to number of persons under each category
             type = 'pie', sort= FALSE, #create pie chart
            marker= list(colors=colors, line = list(color="black", width=1))) %＞% #fills in pie slices
            layout(title="Person's Status in the United States, 1870") #add title
b
#It should be noted that slavery was abolished 1865

Create column figure with Census data

R_pivot %>%
  ggplot(aes(y = Values, #Create chart of persons over time in the united states
             x = year,
             color = Person_Status))+
  geom_col()+ # create column plot 
  labs(title = "Slavery in the United States", #add titles and labels
       subtitle = "Fiscal Years 1790 - 1870",
       caption = " Anthony Starks, Allen Hillery Sekou Tyler \n https://github.com/rfordatascience/tidytuesday/tree/master/data/2021/2021-02-16.", #site data source
       x = "Year", y = "Persons")+ #rename axis's
  theme_minimal() + #add theme
  ggsave(here("TidyT_2","outputs", "Slavery in the United States.png"), #save to outputs
         width = 7, height = 5)